113 lines
7.0 KiB
Markdown
113 lines
7.0 KiB
Markdown
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---
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base_model: pints-ai/1.5-Pints-16K-v0.1
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datasets:
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- pints-ai/Expository-Prose-V1
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- HuggingFaceH4/ultrachat_200k
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- Open-Orca/SlimOrca-Dedup
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- meta-math/MetaMathQA
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- HuggingFaceH4/deita-10k-v0-sft
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- WizardLM/WizardLM_evol_instruct_V2_196k
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- togethercomputer/llama-instruct
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- LDJnr/Capybara
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- HuggingFaceH4/ultrafeedback_binarized
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extra_gated_fields:
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Company: text
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Country: country
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I agree to use this model for in accordance to the afore-mentioned Terms of Use: checkbox
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I want to use this model for:
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options:
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- Research
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- Education
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- label: Other
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value: other
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type: select
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Specific date: date_picker
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extra_gated_prompt: Though best efforts has been made to ensure, as much as possible,
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that all texts in the training corpora are royalty free, this does not constitute
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a legal guarantee that such is the case. **By using any of the models, corpora or
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part thereof, the user agrees to bear full responsibility to do the necessary due
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diligence to ensure that he / she is in compliance with their local copyright laws.
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Additionally, the user agrees to bear any damages arising as a direct cause (or
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otherwise) of using any artifacts released by the pints research team, as well as
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full responsibility for the consequences of his / her usage (or implementation)
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of any such released artifacts. The user also indemnifies Pints Research Team (and
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any of its members or agents) of any damage, related or unrelated, to the release
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or subsequent usage of any findings, artifacts or code by the team. For the avoidance
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of doubt, any artifacts released by the Pints Research team are done so in accordance
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with the 'fair use' clause of Copyright Law, in hopes that this will aid the research
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community in bringing LLMs to the next frontier.
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language:
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- en
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library_name: transformers
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license: mit
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quantized_by: mradermacher
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/pints-ai/1.5-Pints-16K-v0.1
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<!-- provided-files -->
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static quants are available at https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 0.5 | mostly desperate |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 0.7 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.7 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.8 | IQ3_XS probably better |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 0.8 | beats Q3_K* |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 0.8 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.9 | IQ3_S probably better |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.9 | IQ3_M probably better |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.1 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.2 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.2 | |
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| [GGUF](https://huggingface.co/mradermacher/1.5-Pints-16K-v0.1-i1-GGUF/resolve/main/1.5-Pints-16K-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 1.4 | practically like static Q6_K |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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<!-- end -->
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